import pandas as pd
#绘制用户性别比例
from pyecharts.charts import Pie
def g_gender():
    f=open("C:/Users/l'd/Desktop/可视化/py作业/gender.csv",'r',encoding='utf-8-sig')
    data=pd.read_csv(f,header=0)

    x=data.iloc[:,0]
    y=data.iloc[:,1]
    pie=(Pie().add("",[list(z) for z in zip(x,y)],radius=[60,100]).set_colors(["silver", "pink"]).set_global_opts(title_opts=opts.TitleOpts(title="评论用户性别比"), 
    legend_opts=opts.LegendOpts(pos_top="30",pos_left="4%")).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}%"))) 
    pie.render("性别比例.html")
'''

'''
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Pie
#绘制评论日期：
def g_date(): 
    f=open('C:/Users/l'd/Desktop/可视化/py作业/result.csv','r',encoding='utf-8')
    data=pd.read_csv(f)
    x=data.iloc[:,0]
    y=data.iloc[:,1]
    pie=(Pie()
    .add("",[list(z) for z in zip(x,y)])
    .set_global_opts(title_opts=opts.TitleOpts(title="评论日期分布"),
    legend_opts=opts.LegendOpts(pos_left="80%"))) 
    pie.render("评论日期.html")
'''


'''
#评论时间分布图
import pandas as pd
from pyecharts  import Line
def g_time():
    df = pd.read_csv("C:/Users/l'd/Desktop/可视化/py作业/time.csv", header=0, encoding='utf-8-sig')
    print(df.info())
    df['time'] = [int(i.split(':')[0]) for i in df['时间']]
    # 分组汇总
    date_message = df.groupby(['time'])
    date_com = date_message['time'].agg(['count'])
    date_com.reset_index(inplace=True)
    # 绘制分布图
    attr = [str(j)+":00" for j in date_com['time']]
    v1 = date_com['count']
    line =Line("评论的时间分布", title_pos='center', title_top='10', width=900, height=500)
    line.add("", attr, v1, is_smooth=True, is_fill=True,
        area_color="#3299CC", is_xaxislabel_align=True, 
        xaxis_min="dataMin", area_opacity=0.6, 

        mark_point=['max', 'min', 'average'], mark_point_symbol="pin",
        mark_point_symbolsize=50,line_width=2,
        )
        
    line.render("评论的时间分布.html")
'''



'''
#用户属地分布
import pandas as pd
from pyecharts import Map
def g_map():
    df = pd.read_csv("C:/Users/l'd/Desktop/可视化/py作业/all_comments.csv", header=0, encoding='utf-8-sig')
    df['location'] = df['用户城市']#
    # 分组汇总
    loc_message = df.groupby(['location'])
    loc_com = loc_message['location'].agg(['count'])
    loc_com.reset_index(inplace=True)
    # 绘制地图
    a= [i for i in loc_com['location']]
    v= [i for i in loc_com['count']]

    map = Map("用户的地区分布图", title_pos='center', title_top=1)
    map.add("", a, v, maptype="china", is_visualmap=True, visual_text_color="blue", is_map_symbol_show=False, visual_range=[0, 2500])
    map.render('用户的地区分布图.html')

'''

'''
词云图
#
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import random
import jieba
from PIL import Image
import numpy as np

# 设置文本随机颜色
def random_color_func(word=None, font_size=None, position=None, orientation=None, font_path=None, random_state=None):
    h, s, l = random.choice([(2,100,50),(62,89,36),(205,73,59)])
    return "hsl({}, {}%, {}%)".format(h, s, l)

def get_wc():
    # 读取信息
    df1= pd.read_csv("C:/Users/l'd/Desktop/可视化/py作业/all_comments.csv", header=0, encoding='utf-8-sig',quoting=3,error_bad_lines=False)
    df=df1['评论内容']
    df=df.dropna()
    words = pd.read_csv("C:/Users/l'd/Desktop/可视化/csstop.txt", encoding='utf-8=sig', sep='\t', names=['stopword'],quoting=3,error_bad_lines=False)
# 分词
    text = ''
    for line in df:
        text += ' '.join(jieba.cut(str(line), cut_all=False))
    # 停用词
    stopwords = set('')
    stopwords.update('事情','这是','说','地方','干','做','发生','事','拆','想','请','真','发现','太', '有人','干什么')
    stopwords.update(words['stopword'])
#背景
    backgroud_Image = np.array(Image.open("C:/Users/l'd/Desktop/可视化/z.png"))
    mask=backgroud_Image 

    wc = WordCloud(
                    background_color='black',
                    mask=mask,
                    font_path='FZSTK.TTF',
                    max_words=2000,
                    max_font_size=500,
                    min_font_size=15,
                    color_func=random_color_func,
                    prefer_horizontal=1,
                    random_state=60,
                    stopwords=stopwords
                    )
    plt.imshow(wc)
    plt.axis('off')
    wc.to_file("词云.png")
    print('成功!')

'''
'''
#评论情感分布图：
import pandas as pd
from pyecharts.charts import Bar,Line
from pyecharts import options as opts
import snownlp

def feci():#计算
    df1= pd.read_csv("C:/Users/l'd/Desktop/可视化/py作业/all_comments.csv", header=0, encoding='utf-8-sig',quoting=3,error_bad_lines=False)
    df=df1.iloc[:,[1,8]]
    df.columns=['id','com1']
    df = df.drop_duplicates() 
    df=df.dropna()
    df['com2'] = df['com1'].str.extract(r"([\u4e00-\u9fa5]+)")
    df = df.dropna()
    df['score'] = df["com2"].apply(lambda i:snownlp.SnowNLP(i).sentiments)
    dataframe = pd.DataFrame(df)
    dataframe.to_csv("./s.csv", encoding='utf_8_sig', mode='a', index=False, sep=',', header=0 )


def fl(i):
    x=float(i)
    return format(x,"0.1f")

def xt():
    df1 = pd.read_csv("C:/Users/l'd/Desktop/可视化/s.csv", header=0, encoding='utf-8-sig')
    df=df1.iloc[:,[0,3]]
    df.columns=['id','com']
    df['s'] = [fl(i) for i in df['com']]
# 分组汇总
    date_message = df.groupby(['s'])
    date_c= date_message['s'].agg(['count'])
    date_c.reset_index(inplace=True)
    i=[x for x in date_c['count']]
    e=[x for x in date_c['s']]

    bar =Bar(init_opts=opts.InitOpts(width="800px", height="600px"))
    bar.add_xaxis(e)
    bar.add_yaxis(series_name='数量',
                  y_axis=i,
                  label_opts=opts.LabelOpts(is_show=True),
                  itemstyle_opts=opts.ItemStyleOpts('black')
                  )
    bar.set_global_opts(title_opts=opts.TitleOpts(title="情感倾向分布"),                       
                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis"),                      
                        xaxis_opts=opts.AxisOpts(name='情感得分',axislabel_opts={"rotate": 16},           
                        axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow")),
                        yaxis_opts=opts.AxisOpts(
                        name="数量",
                        max_=3000,
                        #axislabel_opts=opts.LabelOpts(formatter="{value} ")
                        ))
    # 折线图
    line = Line()
    line.add_xaxis(e)
    line.add_yaxis(
                    series_name='数量',
                    itemstyle_opts=opts.ItemStyleOpts("#E6E6FA"),
                    y_axis=i,
                    is_smooth=True,
                    label_opts=opts.LabelOpts(is_show=False),
                    areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
                    z=2
                       )
    
    # 把折线图叠到条形图上面
    bar.overlap(line).render_notebook()
    # 保存为html文件.overlap(line)
    bar.render("C:/Users/l'd/Desktop/可视化/py作业/1情感分布.html")

if __name__=="__main__":
    feci()
    xt()